#include "network.hpp"
#include "detection_layer.hpp"
#include "cost_layer.hpp"
#include "utils.hpp"
#include "parser.hpp"
#include "box.hpp"
#include "demo.hpp"
#include "data.hpp"

#include "darknet_internal.hpp"

char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};

void train_yolo(char *cfgfile, char *weightfile)
{
	char* train_images = "data/voc/train.txt";
	char* backup_directory = "backup/";
	srand(time(0));
	char *base = basecfg(cfgfile);
	printf("%s\n", base);
	float avg_loss = -1;
	network net = parse_network_cfg(cfgfile);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
	int imgs = net.batch*net.subdivisions;
	int i = *net.seen/imgs;
	data train, buffer;


	layer l = net.layers[net.n - 1];

	int side = l.side;
	int classes = l.classes;
	float jitter = l.jitter;

	list *plist = get_paths(train_images);
	//int N = plist->size;
	char **paths = (char **)list_to_array(plist);

	load_args args = {0};
	args.w = net.w;
	args.h = net.h;
	args.paths = paths;
	args.n = imgs;
	args.m = plist->size;
	args.classes = classes;
	args.jitter = jitter;
	args.num_boxes = side;
	args.d = &buffer;
	args.type = REGION_DATA;

	args.angle = net.angle;
	args.exposure = net.exposure;
	args.saturation = net.saturation;
	args.hue = net.hue;

	pthread_t load_thread = load_data_in_thread(args);
	clock_t time;
	//while(i*imgs < N*120){
	while(get_current_batch(net) < net.max_batches){
		i += 1;
		time=clock();
		pthread_join(load_thread, 0);
		train = buffer;
		load_thread = load_data_in_thread(args);

		printf("Loaded: %lf seconds\n", sec(clock()-time));

		time=clock();
		float loss = train_network(net, train);
		if (avg_loss < 0) avg_loss = loss;
		avg_loss = avg_loss*.9 + loss*.1;

		printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
		if(i%1000==0 || (i < 1000 && i%100 == 0)){
			char buff[256];
			sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
			save_weights(net, buff);
		}
		free_data(train);
	}
	char buff[256];
	sprintf(buff, "%s/%s_final.weights", backup_directory, base);
	save_weights(net, buff);
}

void print_yolo_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h)
{
	int i, j;
	for(i = 0; i < total; ++i){
		float xmin = boxes[i].x - boxes[i].w/2.;
		float xmax = boxes[i].x + boxes[i].w/2.;
		float ymin = boxes[i].y - boxes[i].h/2.;
		float ymax = boxes[i].y + boxes[i].h/2.;

		if (xmin < 0) xmin = 0;
		if (ymin < 0) ymin = 0;
		if (xmax > w) xmax = w;
		if (ymax > h) ymax = h;

		for(j = 0; j < classes; ++j){
			if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j],
					xmin, ymin, xmax, ymax);
		}
	}
}

void validate_yolo(char *cfgfile, char *weightfile)
{
	network net = parse_network_cfg(cfgfile);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	set_batch_network(&net, 1);
	fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
	srand(time(0));

	char *base = "results/comp4_det_test_";
	//list *plist = get_paths("data/voc.2007.test");
	list* plist = get_paths("data/voc/2007_test.txt");
	//list *plist = get_paths("data/voc.2012.test");
	char **paths = (char **)list_to_array(plist);

	layer l = net.layers[net.n-1];
	int classes = l.classes;

	int j;
	FILE** fps = (FILE**)xcalloc(classes, sizeof(FILE*));
	for(j = 0; j < classes; ++j){
		char buff[1024];
		snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
		fps[j] = fopen(buff, "w");
	}
	box* boxes = (box*)xcalloc(l.side * l.side * l.n, sizeof(box));
	float** probs = (float**)xcalloc(l.side * l.side * l.n, sizeof(float*));
	for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = (float*)xcalloc(classes, sizeof(float));

	int m = plist->size;
	int i=0;
	int t;

	float thresh = .001;
	int nms = 1;
	float iou_thresh = .5;

	int nthreads = 8;
	image* val = (image*)xcalloc(nthreads, sizeof(image));
	image* val_resized = (image*)xcalloc(nthreads, sizeof(image));
	image* buf = (image*)xcalloc(nthreads, sizeof(image));
	image* buf_resized = (image*)xcalloc(nthreads, sizeof(image));
	pthread_t* thr = (pthread_t*)xcalloc(nthreads, sizeof(pthread_t));

	load_args args = {0};
	args.w = net.w;
	args.h = net.h;
	args.type = IMAGE_DATA;

	for(t = 0; t < nthreads; ++t){
		args.path = paths[i+t];
		args.im = &buf[t];
		args.resized = &buf_resized[t];
		thr[t] = load_data_in_thread(args);
	}
	time_t start = time(0);
	for(i = nthreads; i < m+nthreads; i += nthreads){
		fprintf(stderr, "%d\n", i);
		for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
			pthread_join(thr[t], 0);
			val[t] = buf[t];
			val_resized[t] = buf_resized[t];
		}
		for(t = 0; t < nthreads && i+t < m; ++t){
			args.path = paths[i+t];
			args.im = &buf[t];
			args.resized = &buf_resized[t];
			thr[t] = load_data_in_thread(args);
		}
		for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
			char *path = paths[i+t-nthreads];
			char *id = basecfg(path);
			float *X = val_resized[t].data;
			network_predict(net, X);
			int w = val[t].w;
			int h = val[t].h;
			get_detection_boxes(l, w, h, thresh, probs, boxes, 0);
			if (nms) do_nms_sort_v2(boxes, probs, l.side*l.side*l.n, classes, iou_thresh);
			print_yolo_detections(fps, id, boxes, probs, l.side*l.side*l.n, classes, w, h);
			free(id);
			free_image(val[t]);
			free_image(val_resized[t]);
		}
	}

	if (val) free(val);
	if (val_resized) free(val_resized);
	if (buf) free(buf);
	if (buf_resized) free(buf_resized);
	if (thr) free(thr);

	fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
	if (fps) {
		for(j = 0; j < classes; ++j){
			fclose(fps[j]);
		}
		free(fps);
	}
}

void validate_yolo_recall(char *cfgfile, char *weightfile)
{
	network net = parse_network_cfg(cfgfile);
	if(weightfile){
		load_weights(&net, weightfile);
	}
	set_batch_network(&net, 1);
	fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
	srand(time(0));

	list *plist = get_paths("data/voc.2007.test");
	char **paths = (char **)list_to_array(plist);

	layer l = net.layers[net.n-1];
	int classes = l.classes;
	int side = l.side;

	int j, k;
	box* boxes = (box*)xcalloc(side * side * l.n, sizeof(box));
	float** probs = (float**)xcalloc(side * side * l.n, sizeof(float*));
	for(j = 0; j < side*side*l.n; ++j) {
		probs[j] = (float*)xcalloc(classes, sizeof(float));
	}

	int m = plist->size;
	int i=0;

	float thresh = .001;
	float iou_thresh = .5;
	float nms = 0;

	int total = 0;
	int correct = 0;
	int proposals = 0;
	float avg_iou = 0;

	for(i = 0; i < m; ++i){
		char *path = paths[i];
		image orig = load_image_color(path, 0, 0);
		image sized = resize_image(orig, net.w, net.h);
		char *id = basecfg(path);
		network_predict(net, sized.data);
		get_detection_boxes(l, orig.w, orig.h, thresh, probs, boxes, 1);
		if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms);

		char labelpath[4096];
		replace_image_to_label(path, labelpath);

		int num_labels = 0;
		box_label *truth = read_boxes(labelpath, &num_labels);
		for(k = 0; k < side*side*l.n; ++k){
			if(probs[k][0] > thresh){
				++proposals;
			}
		}
		for (j = 0; j < num_labels; ++j) {
			++total;
			box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
			float best_iou = 0;
			for(k = 0; k < side*side*l.n; ++k){
				float iou = box_iou(boxes[k], t);
				if(probs[k][0] > thresh && iou > best_iou){
					best_iou = iou;
				}
			}
			avg_iou += best_iou;
			if(best_iou > iou_thresh){
				++correct;
			}
		}

		fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
		free(id);
		free(truth);
		free_image(orig);
		free_image(sized);
	}
}

void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
{
	network net = parse_network_cfg(cfgfile);
	if(weightfile)
	{
		load_weights(&net, weightfile);
	}
	detection_layer l = net.layers[net.n-1];
	set_batch_network(&net, 1);
	srand(2222222);
	char buff[256];
	char *input = buff;
	int j;
	float nms=.4;
	box* boxes = (box*)xcalloc(l.side * l.side * l.n, sizeof(box));
	float** probs = (float**)xcalloc(l.side * l.side * l.n, sizeof(float*));
	for(j = 0; j < l.side*l.side*l.n; ++j) {
		probs[j] = (float*)xcalloc(l.classes, sizeof(float));
	}
	while(1){
		if(filename){
			strncpy(input, filename, 256);
		} else {
			printf("Enter Image Path: ");
			fflush(stdout);
			input = fgets(input, 256, stdin);
			if(!input) return;
			strtok(input, "\n");
		}
		image im = load_image_color(input,0,0);
		image sized = resize_image(im, net.w, net.h);
		float *X = sized.data;
		clock_t time=clock();
		network_predict(net, X);
		printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
		get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0);
		if (nms) do_nms_sort_v2(boxes, probs, l.side*l.side*l.n, l.classes, nms);
		//draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20);
		draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, 20);
		save_image(im, "predictions");
		show_image(im, "predictions");

		free_image(im);
		free_image(sized);

		wait_until_press_key_cv();
		destroy_all_windows_cv();

		if (filename) break;
	}
	free(boxes);
	for(j = 0; j < l.side*l.side*l.n; ++j) {
		free(probs[j]);
	}
	free(probs);
}

void run_yolo(int argc, char **argv)
{
	int dont_show = (Darknet::CfgAndState::get().is_shown ? 1 : 0);
//	int dont_show = find_arg(argc, argv, "-dont_show");
	int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1);
	int json_port = find_int_arg(argc, argv, "-json_port", -1);
	char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);
	char *prefix = find_char_arg(argc, argv, "-prefix", 0);
	float thresh = find_float_arg(argc, argv, "-thresh", .2);
	float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
	int cam_index = find_int_arg(argc, argv, "-c", 0);
	int frame_skip = find_int_arg(argc, argv, "-s", 0);
	int ext_output = find_arg(argc, argv, "-ext_output");
	if(argc < 4){
		fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
		return;
	}

	char *cfg = argv[3];
	char *weights = (argc > 4) ? argv[4] : 0;
	char *filename = (argc > 5) ? argv[5]: 0;
	if(0==strcmp(argv[2], "test")) test_yolo(cfg, weights, filename, thresh);
	else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights);
	else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights);
	else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights);
	else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, hier_thresh, cam_index, filename, voc_names, 20, 1, frame_skip,
		prefix, out_filename, mjpeg_port, 0, json_port, dont_show, ext_output, 0, 0, 0, 0, 0);
}
